





import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def      A__       (    SCREAMING_SNAKE_CASE_   :      Union[str, Any]				,	SCREAMING_SNAKE_CASE_   :      List[Any]						)  ->  str:






   """simple docstring"""







   if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
      # expert layer
      _UpperCAmelCase           =   flax_key_tuple[:-1] + ('''weight''',)
      _UpperCAmelCase           =   torch.permute(SCREAMING_SNAKE_CASE_				,	(0, 2, 1)						)
   elif flax_key_tuple[-1] == "kernel" and ".".join(SCREAMING_SNAKE_CASE_						):
      # linear layer
      _UpperCAmelCase           =   flax_key_tuple[:-1] + ('''weight''',)
      _UpperCAmelCase           =   flax_tensor.T
   elif flax_key_tuple[-1] in ["scale", "embedding"]:
      _UpperCAmelCase           =   flax_key_tuple[:-1] + ('''weight''',)

   return flax_key_tuple, flax_tensor
def      A__       (    SCREAMING_SNAKE_CASE_   :      Optional[int]				,	SCREAMING_SNAKE_CASE_   :      Dict				,	SCREAMING_SNAKE_CASE_   :      List[Any]						)  ->  List[Any]:






   """simple docstring"""







   if "metadata" in layer:
      _UpperCAmelCase           =   layer.split('''metadata'''						)
      _UpperCAmelCase           =   ''''''.join(split_layer[0]						)[:-1]
      _UpperCAmelCase           =   [tuple(('''metadata''' + split_layer[1]).split('''/'''						)						)]
   elif "kvstore" in layer:
      _UpperCAmelCase           =   layer.split('''kvstore'''						)
      _UpperCAmelCase           =   ''''''.join(split_layer[0]						)[:-1]
      _UpperCAmelCase           =   [tuple(('''kvstore''' + split_layer[1]).split('''/'''						)						)]

   else:
      _UpperCAmelCase           =   layer.split('''/'''						)
      _UpperCAmelCase           =   '''/'''.join(split_layer[:-1]						)
      _UpperCAmelCase           =   (split_layer[-1],)

   if "kvstore/path" in layer:
      _UpperCAmelCase           =   F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
   elif "kvstore/driver" in layer:
      _UpperCAmelCase           =   '''file'''
   else:
      _UpperCAmelCase           =   checkpoint_info[layer]

   return curr_real_layer_name, split_layer, content
def      A__       (    SCREAMING_SNAKE_CASE_   :      Union[str, Any]				,	SCREAMING_SNAKE_CASE_   :      Optional[int]						)  ->  Any:






   """simple docstring"""







   _UpperCAmelCase           =   rename_keys(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   {}
   for k, v in current_block.items():
      _UpperCAmelCase           =   v
   _UpperCAmelCase           =   new_current_block
   torch.save(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)
def      A__       (    SCREAMING_SNAKE_CASE_   :      str				,	SCREAMING_SNAKE_CASE_   :      List[str]				,	SCREAMING_SNAKE_CASE_   :      List[Any]				,	SCREAMING_SNAKE_CASE_   :      Optional[int]				,	SCREAMING_SNAKE_CASE_   :      str = WEIGHTS_NAME						)  ->  Tuple:






   """simple docstring"""







   _UpperCAmelCase           =   convert_file_size_to_int(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   []
   _UpperCAmelCase           =   {}
   _UpperCAmelCase           =   0
   _UpperCAmelCase           =   0

   os.makedirs(SCREAMING_SNAKE_CASE_				,	exist_ok=SCREAMING_SNAKE_CASE_						)
   with gfile.GFile(switch_checkpoint_path + '''/checkpoint'''				,	'''rb'''						) as fp:
      _UpperCAmelCase           =   serialization.msgpack_restore(fp.read()						)['''optimizer''']['''target''']
      _UpperCAmelCase           =   flatten_dict(SCREAMING_SNAKE_CASE_				,	sep='''/'''						)

   _UpperCAmelCase           =   {}
   for layer in checkpoint_info.keys():
      _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase           =   get_key_and_tensorstore_dict(
          SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)
      if curr_real_layer_name in all_layers:
         _UpperCAmelCase           =   content
      else:
         _UpperCAmelCase           =   {split_layer[-1]: content}

   for key in all_layers.keys():
      # open tensorstore file
      _UpperCAmelCase           =   ts.open(unflatten_dict(all_layers[key]						)						).result().read().result()
      _UpperCAmelCase           =   torch.tensor(SCREAMING_SNAKE_CASE_						)
      _UpperCAmelCase           =   raw_weights.numel() * dtype_byte_size(raw_weights.dtype						)

      # use the renaming pattern from the small conversion scripts
      _UpperCAmelCase       ,      _UpperCAmelCase           =   rename_base_flax_keys(tuple(key.split('''/'''						)						)				,	SCREAMING_SNAKE_CASE_						)
      _UpperCAmelCase           =   '''/'''.join(SCREAMING_SNAKE_CASE_						)

      # If this weight is going to tip up over the maximal size, we split.
      if current_block_size + weight_size > max_shard_size:
         _UpperCAmelCase           =   os.path.join(
             SCREAMING_SNAKE_CASE_				,	weights_name.replace('''.bin'''				,	F'''-{len(SCREAMING_SNAKE_CASE_						)+1:05d}-of-???.bin'''						)						)
         rename_and_save_block(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)
         sharded_state_dicts.append(current_block.keys()						)
         del current_block
         _UpperCAmelCase           =   {}
         _UpperCAmelCase           =   0

      _UpperCAmelCase           =   raw_weights.to(getattr(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)						)
      current_block_size += weight_size
      total_size += weight_size

   # Add the last block
   _UpperCAmelCase           =   os.path.join(SCREAMING_SNAKE_CASE_				,	weights_name.replace('''.bin'''				,	F'''-{len(SCREAMING_SNAKE_CASE_						)+1:05d}-of-???.bin'''						)						)
   rename_and_save_block(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)
   sharded_state_dicts.append(current_block.keys()						)

   # If we only have one shard, we return it
   if len(SCREAMING_SNAKE_CASE_						) == 1:
      return {weights_name: sharded_state_dicts[0]}, None

   # Otherwise, let's build the index
   _UpperCAmelCase           =   {}
   _UpperCAmelCase           =   {}
   for idx, shard in enumerate(SCREAMING_SNAKE_CASE_						):
      _UpperCAmelCase           =   weights_name.replace(
          '''.bin'''				,	F'''-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_						):05d}.bin'''						)  # len(sharded_state_dicts):05d}
      _UpperCAmelCase           =   os.path.join(SCREAMING_SNAKE_CASE_				,	weights_name.replace('''.bin'''				,	F'''-{idx+1:05d}-of-???.bin'''						)						)
      os.rename(SCREAMING_SNAKE_CASE_				,	os.path.join(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)						)
      _UpperCAmelCase           =   shard
      for key in shard:
         _UpperCAmelCase           =   shard_file

    # Add the metadata
   _UpperCAmelCase           =   {'''total_size''': total_size}
   _UpperCAmelCase           =   {'''metadata''': metadata, '''weight_map''': weight_map}

   with open(os.path.join(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)				,	'''w'''				,	encoding='''utf-8'''						) as f:
      _UpperCAmelCase           =   json.dumps(SCREAMING_SNAKE_CASE_				,	indent=2				,	sort_keys=SCREAMING_SNAKE_CASE_						) + '''\n'''
      f.write(SCREAMING_SNAKE_CASE_						)

   return metadata, index


if __name__ == "__main__":
   UpperCAmelCase_           =	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--switch_t5x_checkpoint_path",
       default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
       type=str,
       required=False,
       help="Path to a directory containing a folder per layer. Follows the original Google format.",
   )
   parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
   parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
       type=str,
       required=False,
       help="Path to the output pytorch model.",
   )
   UpperCAmelCase_           =	parser.parse_args()
   shard_on_the_fly(
       args.switch_tax_checkpoint_path,
       args.pytorch_dump_folder_path,
       args.max_shard_size,
       args.dtype,
   )
def      A__       (    )  ->  Dict:






   """simple docstring"""







   from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

   _UpperCAmelCase           =   SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''						)
   config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''						)
   _UpperCAmelCase           =   SwitchTransformersForConditionalGeneration.from_pretrained(
       '''/home/arthur_huggingface_co/transformers/switch_converted'''				,	device_map='''auto'''						)

   _UpperCAmelCase           =   TaTokenizer.from_pretrained('''t5-small'''						)
   _UpperCAmelCase           =   '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

   _UpperCAmelCase           =   tokenizer(SCREAMING_SNAKE_CASE_				,	return_tensors='''pt'''						).input_ids
   _UpperCAmelCase           =   model.generate(SCREAMING_SNAKE_CASE_				,	decoder_start_token_id=0						)
   print(tokenizer.decode(out[0]						)						)







'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : str      			=     CycleDiffusionPipeline
  _a  : int      			=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      '''negative_prompt''',
      '''height''',
      '''width''',
      '''negative_prompt_embeds''',
  }
  _a  : str      			=     PipelineTesterMixin.required_optional_params - {'''latents'''}
  _a  : str      			=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}       )
  _a  : Optional[Any]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS
  _a  : Optional[int]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS







  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			UNetaDConditionModel(
             block_out_channels=(32, 64)   ,	layers_per_block=2   ,	sample_size=32   ,	in_channels=4   ,	out_channels=4   ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,	cross_attention_dim=32   ,	)
         a_						     =			DDIMScheduler(
             beta_start=0.00_085   ,	beta_end=0.012   ,	beta_schedule='scaled_linear'   ,	num_train_timesteps=1_000   ,	clip_sample=UpperCamelCase__   ,	set_alpha_to_one=UpperCamelCase__   ,	)
         torch.manual_seed(0	)
         a_						     =			AutoencoderKL(
             block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,	latent_channels=4   ,	)
         torch.manual_seed(0	)
         a_						     =			CLIPTextConfig(
             bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=32   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1_000   ,	)
         a_						     =			CLIPTextModel(UpperCamelCase__	)
         a_						     =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

         a_						     =			{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=0	):






         """simple docstring"""


         a_						     =			floats_tensor((1, 3, 32, 32)   ,	rng=random.Random(UpperCamelCase__	)	).to(UpperCamelCase__	)
         a_						     =			image / 2 + 0.5
         if str(UpperCamelCase__	).startswith('mps'	):
                a_						     =			torch.manual_seed(UpperCamelCase__	)
         else:
                a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(UpperCamelCase__	)
         a_						     =			{
             'prompt': 'An astronaut riding an elephant',
             'source_prompt': 'An astronaut riding a horse',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'eta': 0.1,
             'strength': 0.8,
             'guidance_scale': 3,
             'source_guidance_scale': 1,
             'output_type': 'numpy',
         }
         return inputs







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'  # ensure determinism for the device-dependent torch.Generator

         a_						     =			self.get_dummy_components()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @unittest.skipIf(torch_device != 'cuda'   ,	'This test requires a GPU'	)
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.get_dummy_components()
         for name, module in components.items():
                if hasattr(UpperCamelCase__   ,	'half'	):
                       a_						     =			module.half()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_local()







  @unittest.skip('non-deterministic pipeline'	)
  def    _a       (			self	):






         """simple docstring"""


         return super().test_inference_batch_single_identical()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_dict_tuple_outputs_equivalent()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_optional_components()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_attention_slicing_forward_pass()





@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(
             UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__   ,	torch_dtype=torch.floataa   ,	revision='fp16'	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image	).max() < 5e-1







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         assert np.abs(image - expected_image	).max() < 2e-2




